Co-saliency detection aims at discovering the common and salient objects in a group of \(N\)  relevant images \(\mathcal {I}=\lbrace I^{(n)}\rbrace _{n=1}^N\) . It is worth raising that directly constructs a pure transformer-based network for co-saliency detection will produce unsatisfactory performance. In SETR [1]}, which is a pure transformer-based network, Transformers treat the input as 1D sequences and exclusively focus on modeling the global context at all Transformer layers, therefore result in low-resolution features which lack detailed low-level information. And this information cannot be effectively recovered by direct upsampling to the full resolution. In co-saliency detection task, Transformers can well model the relation between different pixels, but can not recover fine detailed information. On the other hand, CNN architectures (e.g.,U-Net [2]}) provide an avenue for extracting low-level visual cues which can well remedy such fine details. To this end, CoSformer employs a CNN-Transformer architecture to leverage both detailed low-level detailed information from CNN features and the relation encoded by Transformers. We hope that the simplicity of our method will attract new researchers to the co-saliency detection community. Our proposed CoSformer architecture is simple and illustrated in Fig.REF .
